SoroushVahidi/scidocs huggingface dataset:
Different models for model matching: An analysis of approaches to support model differencing
(2009)

Calculating differences between models is an important and challenging task in Model Driven Engineering. Model differencing involves a number of steps starting with identifying matching model elements, calculating and representing their differences, and finally visualizing them in an appropriate way. In this paper, we provide an overview of the fundamental steps involved in the model differencing process and summarize the advantages and shortcomings of existing approaches for identifying matching model elements. To assist potential users in selecting one of the existing methods for the problem at stake, we investigate the trade-offs these methods impose in terms of accuracy and effort required to implement each one of them.
Wearable Computing for Health and Fitness: Exploring the Relationship between Data and Human Behaviour
(2015)

Health and fitness wearable technology has recently advanced, making it easier for an individual to monitor their behaviours. Previously self generated data interacts with the user to motivate positive behaviour change, but issues arise when relating this to long term mention of wearable devices. Previous studies within this area are discussed. We also consider a new approach where data is used to support instead of motivate, through monitoring and logging to encourage reflection. Based on issues highlighted, we then make recommendations on the direction in which future work could be most beneficial.
Evaluation of a Tree-based Pipeline Optimization Tool for Automating Data Science
(2016)

As the field of data science continues to grow, there will be an ever-increasing demand for tools that make machine learning accessible to non-experts. In this paper, we introduce the concept of tree-based pipeline optimization for automating one of the most tedious parts of machine learning--pipeline design. We implement an open source Tree-based Pipeline Optimization Tool (TPOT) in Python and demonstrate its effectiveness on a series of simulated and real-world benchmark data sets. In particular, we show that TPOT can design machine learning pipelines that provide a significant improvement over a basic machine learning analysis while requiring little to no input nor prior knowledge from the user. We also address the tendency for TPOT to design overly complex pipelines by integrating Pareto optimization, which produces compact pipelines without sacrificing classification accuracy. As such, this work represents an important step toward fully automating machine learning pipeline design.
Brain basis of early parent-infant interactions: psychology, physiology, and in vivo functional neuroimaging studies.
(2007)

Parenting behavior critically shapes human infants' current and future behavior. The parent-infant relationship provides infants with their first social experiences, forming templates of what they can expect from others and how to best meet others' expectations. In this review, we focus on the neurobiology of parenting behavior, including our own functional magnetic resonance imaging (fMRI) brain imaging experiments of parents. We begin with a discussion of background, perspectives and caveats for considering the neurobiology of parent-infant relationships. Then, we discuss aspects of the psychology of parenting that are significantly motivating some of the more basic neuroscience research. Following that, we discuss some of the neurohormones that are important for the regulation of social bonding, and the dysregulation of parenting with cocaine abuse. Then, we review the brain circuitry underlying parenting, proceeding from relevant rodent and nonhuman primate research to human work. Finally, we focus on a study-by-study review of functional neuroimaging studies in humans. Taken together, this research suggests that networks of highly conserved hypothalamic-midbrain-limbic-paralimbic-cortical circuits act in concert to support aspects of parent response to infants, including the emotion, attention, motivation, empathy, decision-making and other thinking that are required to navigate the complexities of parenting. Specifically, infant stimuli activate basal forebrain regions, which regulate brain circuits that handle specific nurturing and caregiving responses and activate the brain's more general circuitry for handling emotions, motivation, attention, and empathy--all of which are crucial for effective parenting. We argue that an integrated understanding of the brain basis of parenting has profound implications for mental health.
Learning to Fuse Things and Stuff
(2018)

We propose an end-to-end learning approach for panoptic segmentation, a novel task unifying instance (things) and semantic (stuff) segmentation. Our model, TASCNet, uses feature maps from a shared backbone network to predict in a single feed-forward pass both things and stuff segmentations. We explicitly constrain these two output distributions through a global things and stuff binary mask to enforce cross-task consistency. Our proposed unified network is competitive with the state of the art on several benchmarks for panoptic segmentation as well as on the individual semantic and instance segmentation tasks.
How to get mature global virtual teams: a framework to improve team process management in distributed software teams
(2010)

Managing global software development teams is not an easy task because of the additional problems and complexities that have to be taken into account. This paper defines VTManager, a methodology that provides a set of efficient practices for global virtual team management in software development projects. These practices integrate software development techniques in global environments with others such as explicit practices for global virtual team management, definition of skills and abilities needed to work in these teams, availability of collaborative work environments and shared knowledge management practices. The results obtained and the lessons learned from implementing VTManager in a pilot project to develop software tools for collaborative work in rural environments are also presented. This project was carried out by geographically distributed teams involving people from seven countries with a high level of virtualness.
An iterative maximum-likelihood polychromatic algorithm for CT
(2001)

A new iterative maximum-likelihood reconstruction algorithm for X-ray computed tomography is presented. The algorithm prevents beam hardening artifacts by incorporating a polychromatic acquisition model. The continuous spectrum of the X-ray tube is modeled as a number of discrete energies. The energy dependence of the attenuation is taken into account by decomposing the linear attenuation coefficient into a photoelectric component and a Compton scatter component. The relative weight of these components is constrained based on prior material assumptions. Excellent results are obtained for simulations and for phantom measurements. Beam-hardening artifacts are effectively eliminated. The relation with existing algorithms is discussed. The results confirm that improving the acquisition model assumed by the reconstruction algorithm results in reduced artifacts. Preliminary results indicate that metal artifact reduction is a very promising application for this new algorithm.
Optimal Charging in Wireless Rechargeable Sensor Networks
(2016)

Recent years have witnessed several new promising technologies to power wireless sensor networks, which motivate some key topics to be revisited. By integrating sensing and computation capabilities to the traditional radio-frequency identification (RFID) tags, the Wireless Identification and Sensing Platform (WISP) is an open-source platform acting as a pioneering experimental platform of wireless rechargeable sensor networks. Different from traditional tags, an RFID-based wireless rechargeable sensor node needs to charge its onboard energy storage above a threshold to power its sensing, computation, and communication components. Consequently, such charging delay imposes a unique design challenge for deploying wireless rechargeable sensor networks. In this paper, we tackle this problem by planning the optimal movement strategy of the mobile RFID reader, such that the time to charge all nodes in the network above their energy threshold is minimized. We first propose an optimal solution using the linear programming (LP) method. To further reduce the computational complexity, we then introduce a heuristic solution with a provable approximation ratio of (1 + θ)/(1 - ε) by discretizing the charging power on a 2-D space. Through extensive evaluations, we demonstrate that our design outperforms the set-cover-based design by an average of 24.7%, whereas the computational complexity is O((N/ε)2). Finally, we consider two practical issues in system implementation and provide guidelines for parameter setting.
CPW to waveguide transition with tapered slotline probe
(2001)

A new CPW to waveguide transition is developed based on the concept of tapered slot antenna and E-plane probe coupling. The transition consists of a tapered slotline probe and a slotline to CPW matching section. The current design has the advantages of broad bandwidth, compact size, low fabrication cost, and high reliability. The characteristics of a prototype transition are investigated by numerical simulation on Duroid substrate and WR-90 waveguide. The back-to-back combination is measured to verify the agreement with the simulated results and realization of this design.
A novel secure hash algorithm for public key digital signature schemes
(2012)

Hash functions are the most widespread among all cryptographic primitives, and are currently used in multiple cryptographic schemes and in security protocols. This paper presents a new Secure Hash Algorithm called (SHA-192). It uses a famous secure hash algorithm given by the National Institute of Standard and Technology (NIST).The basic design of SHA192 is to have the output length of 192.The SHA-192 has been designed to satisfy the different level of enhanced security and to resist the advanced SHA attacks. The security analysis of the SHA-192 is compared to the old one given by NIST and gives more security and excellent results as shown in our discussion. In this paper the digital signature algorithm which is given by NIST has been modified using the proposed algorithms SHA-192. Using proposed SHA-192 hash algorithm a new digital signature schemes is also proposed. The SHA-192 can be used in many applications such s public key cryptosystem, digital signcryption, message authentication code, random generator and in security architecture of upcoming wireless devices like software defined radio etc.
Ethical Challenges in Data-Driven Dialogue Systems
(2018)

The use of dialogue systems as a medium for human-machine interaction is an increasingly prevalent paradigm. A growing number of dialogue systems use conversation strategies that are learned from large datasets. There are well documented instances where interactions with these system have resulted in biased or even offensive conversations due to the data-driven training process. Here, we highlight potential ethical issues that arise in dialogue systems research, including: implicit biases in data-driven systems, the rise of adversarial examples, potential sources of privacy violations, safety concerns, special considerations for reinforcement learning systems, and reproducibility concerns. We also suggest areas stemming from these issues that deserve further investigation. Through this initial survey, we hope to spur research leading to robust, safe, and ethically sound dialogue systems.
Speed-Security Tradeo s in Blockchain Protocols
(2015)

Transaction processing speed is one of the major considerations in cryptocurrencies that are based on proof of work (POW) such as Bitcoin. At an intuitive level it is widely understood that processing speed is at odds with the security aspects of the underlying POW based consensus mechanism of such protocols, nevertheless the tradeo between the two properties is still not well understood. In this work, motivated by recent work [9] in the formal analysis of the Bitcoin backbone protocol, we investigate the tradeo between provable security and transaction processing speed viewing the latter as a function of the block generation rate. We introduce a new formal property of blockchain protocols, called chain growth, and we show it is fundamental for arguing the security of a robust transaction ledger. We strengthen the results of [9] showing for the rst time that reasonable security bounds hold even for the faster (than Bitcoin's) block generation rates that have been adopted by several major alt-coins (including Litecoin, Dogecoin etc.). We then provide a rst formal security proof of the GHOST rule for blockchain protocols. The GHOST rule was put forth in [14] as a mechanism to improve transaction processing speed and a variant of the rule is adopted by Ethereum. Our security analysis of the GHOST backbone matches our new analysis for Bitcoin in terms of the common pre x property but falls short in terms of chain growth where we provide an attack that substantially reduces the chain speed compared to Bitcoin. While our results establish the GHOST variant as a provably secure alternative to standard Bitcoin-like transaction ledgers they also highlight potential shortcomings in terms of processing speed compared to Bitcoin. We nally present attacks and simulation results against blockchain protocols (both for Bitcoin and GHOST) that present natural upper barriers for the speed-security tradeo . By combining our positive and negative results we map the speed/security domain for blockchain protocols and list open problems for future work.
Classification of teeth in cone-beam CT using deep convolutional neural network
(2017)

Dental records play an important role in forensic identification. To this end, postmortem dental findings and teeth conditions are recorded in a dental chart and compared with those of antemortem records. However, most dentists are inexperienced at recording the dental chart for corpses, and it is a physically and mentally laborious task, especially in large scale disasters. Our goal is to automate the dental filing process by using dental x-ray images. In this study, we investigated the application of a deep convolutional neural network (DCNN) for classifying tooth types on dental cone-beam computed tomography (CT) images. Regions of interest (ROIs) including single teeth were extracted from CT slices. Fifty two CT volumes were randomly divided into 42 training and 10 test cases, and the ROIs obtained from the training cases were used for training the DCNN. For examining the sampling effect, random sampling was performed 3 times, and training and testing were repeated. We used the AlexNet network architecture provided in the Caffe framework, which consists of 5 convolution layers, 3 pooling layers, and 2 full connection layers. For reducing the overtraining effect, we augmented the data by image rotation and intensity transformation. The test ROIs were classified into 7 tooth types by the trained network. The average classification accuracy using the augmented training data by image rotation and intensity transformation was 88.8%. Compared with the result without data augmentation, data augmentation resulted in an approximately 5% improvement in classification accuracy. This indicates that the further improvement can be expected by expanding the CT dataset. Unlike the conventional methods, the proposed method is advantageous in obtaining high classification accuracy without the need for precise tooth segmentation. The proposed tooth classification method can be useful in automatic filing of dental charts for forensic identification.
Harvesting rib cartilage grafts for secondary rhinoplasty.
(2008)

BACKGROUND
Reconstruction of the nasal osseocartilaginous framework is the foundation of successful secondary rhinoplasty.


METHODS
Achieving this often requires large quantities of cartilage to correct both contour deformities and functional problems caused by previous procedures. Satisfactory and consistent long-term results rely on using grafts with low resorption rates and sufficient strength to offer adequate support. Auricular cartilage, irradiated cartilage, and alloplastic materials have all been used as implantable grafts with limited success.


RESULTS
In the senior author's experience (J.P.G.), rib cartilage has proven to be a reliable, abundant, and relatively accessible donor with which to facilitate successful secondary rhinoplasty surgery.


CONCLUSIONS
: The authors describe in detail the techniques that they have found to be integral in harvesting rib cartilage grafts for secondary rhinoplasty.
High-Dimensional Continuous Control Using Generalized Advantage Estimation
(2015)

This paper is concerned with developing policy gradient methods that gracefully scale up to challenging problems with high-dimensional state and action spaces. Towards this end, we develop a scheme that uses value functions to substantially reduce the variance of policy gradient estimates, while introducing a tolerable amount of bias. This scheme, which we call generalized advantage estimation (GAE), involves using a discounted sum of temporal difference residuals as an estimate of the advantage function, and can be interpreted as a type of automated cost shaping. It is simple to implement and can be used with a variety of policy gradient methods and value function approximators. Along with this variance-reduction scheme, we use trust region algorithms to optimize the policy and value function, both represented as neural networks. We present experimental results on a number of highly challenging 3D locomotion tasks, where our approach learns complex gaits for bipedal and quadrupedal simulated robots. We also learn controllers for the biped getting up off the ground. In contrast to prior work that uses hand-crafted low-dimensional policy representations, our neural network policies map directly from raw kinematics to joint torques.
Policy Gradient Methods for Robotics
(2006)

The acquisition and improvement of motor skills and control policies for robotics from trial and error is of essential importance if robots should ever leave precisely pre-structured environments. However, to date only few existing reinforcement learning methods have been scaled into the domains of high-dimensional robots such as manipulator, legged or humanoid robots. Policy gradient methods remain one of the few exceptions and have found a variety of applications. Nevertheless, the application of such methods is not without peril if done in an uninformed manner. In this paper, we give an overview on learning with policy gradient methods for robotics with a strong focus on recent advances in the field. We outline previous applications to robotics and show how the most recently developed methods can significantly improve learning performance. Finally, we evaluate our most promising algorithm in the application of hitting a baseball with an anthropomorphic arm
Deep reinforcement learning for robotic manipulation with asynchronous off-policy updates
(2017)

Reinforcement learning holds the promise of enabling autonomous robots to learn large repertoires of behavioral skills with minimal human intervention. However, robotic applications of reinforcement learning often compromise the autonomy of the learning process in favor of achieving training times that are practical for real physical systems. This typically involves introducing hand-engineered policy representations and human-supplied demonstrations. Deep reinforcement learning alleviates this limitation by training general-purpose neural network policies, but applications of direct deep reinforcement learning algorithms have so far been restricted to simulated settings and relatively simple tasks, due to their apparent high sample complexity. In this paper, we demonstrate that a recent deep reinforcement learning algorithm based on off-policy training of deep Q-functions can scale to complex 3D manipulation tasks and can learn deep neural network policies efficiently enough to train on real physical robots. We demonstrate that the training times can be further reduced by parallelizing the algorithm across multiple robots which pool their policy updates asynchronously. Our experimental evaluation shows that our method can learn a variety of 3D manipulation skills in simulation and a complex door opening skill on real robots without any prior demonstrations or manually designed representations.
Infinite-Horizon Policy-Gradient Estimation
(2001)

Gradient-based approaches to direct policy search in reinf orcement learning have received much recent attention as a means to solve problems of partial observability and to avoid some of the problems associated with policy degradation in value-f unction methods. In this paper we introduceGPOMDP, a simulation-based algorithm for generating a bi sedestimate of the gradient of theaverage rewardin Partially Observable Markov Decision Processes ( POMDPs) controlled by parameterized stochastic policies. A similar algorithm wa s proposed by Kimura, Yamamura, and Kobayashi (1995). The algorithm’s chief advantages are tha t i requires storage of only twice the number of policy parameters, uses one free parameter 2 [0; 1) (which has a natural interpretation in terms of bias-variance trade-off), and requires no knowl edge of the underlying state. We prove convergence of GPOMDP, and show how the correct choice of the parameter is related to the mixing timeof the controlledPOMDP. We briefly describe extensions of GPOMDP to controlled Markov chains, continuous state, observation and control s paces, multiple-agents, higher-order derivatives, and a version for training stochastic policie s with internal states. In a companion paper (Baxter, Bartlett, & Weaver, 2001) we show how the gradient e stimates generated by GPOMDP can be used in both a traditional stochastic gradient algori thm and a conjugate-gradient procedure to find local optima of the average reward.
Privacy-Preserving Public Auditing For Secure Cloud Storage
(2014)

By using Cloud storage, users can access applications, services, software whenever they requires over the internet. Users can put their data remotely to cloud storage and get benefit of on-demand services and application from the resources. The cloud must have to ensure data integrity and security of data of user. The issue about cloud storage is integrity and privacy of data of user can arise. To maintain to overkill this issue here, we are giving public auditing process for cloud storage that users can make use of a third-party auditor (TPA) to check the integrity of data. Not only verification of data integrity, the proposed system also supports data dynamics. The work that has been done in this line lacks data dynamics and true public auditability. The auditing task monitors data modifications, insertions and deletions. The proposed system is capable of supporting public auditability, data dynamics and Multiple TPA are used for the auditing process. We also extend our concept to ring signatures in which HARS scheme is used. Merkle Hash Tree is used to improve block level authentication. Further we extend our result to enable the TPA to perform audits for multiple users simultaneously through Batch auditing.
Design Of Ternary Logic Gates Using CNTFET
(2015)

This paper presents a novel design of ternary logic gates like STI,PTI,NTI,NAND and NOR using carbon nanotube field effect transistors. Ternary logic is a promising alternative to the conventional binary logic design technique, since it is possible to accomplish simplicity and energy efficiency in modern digital design due to the reduced circuit overhead such as interconnects and chip area. In this paper novel design of basic logic gates for ternary logic based on CNTFET, is proposed. Keywords— Carbon nano-tube Field Effect Transistor(CNTFET),MVL(multi valued logic),Ternary logic,STI,NTI,PTI